Pore structure parameter analysis method for metal or ceramic material

Abstract

The invention discloses a pore structure parameter analysis method for a metal or ceramic material, which comprises the following steps: continuously cutting the material for multiple times to obtain a plurality of samples, and sequentially polishing each sample to obtain a high-quality target sample; using a scanning electron microscope to obtain a grayscale image of each target sample at a preset magnification, and generating a plurality of continuous section grayscale images; a binary image is generated by adjusting the image contrast and applying an adaptive threshold segmentation algorithm, and then connected domain analysis is executed to extract structural parameters of each pore; identifying and matching the same pore appearing on the adjacent binary images to form a continuous three-dimensional pore model, and determining the type of the pore according to the continuous three-dimensional pore model; finally, a pore structure parameter data set is generated by calculating the ratio of the diameter of the equivalent circle and the sum of the pore areas to the area of the area, synchronous and accurate measurement of the pore morphology and the porosity is ensured through the steps, and more comprehensive and accurate data support is provided for scientific research of materials.

Description

Technical Field

[0001] The present invention relates to the field of metal or ceramic materials, and in particular to a pore structure parameter analysis method for metal or ceramic materials. Background Art

[0002] Existing porosity detection methods have significant defects, which limit their application in high-precision material analysis. First, although the water displacement method (ISO 18754:2020 standard method) can provide measurement results of total porosity, it cannot distinguish between open pores and closed pores, and its detection sensitivity is insufficient for micropores smaller than 1μm. Secondly, the mercury intrusion method will damage the sample structure during the detection process, and the Washburn equation has theoretical errors when calculating nanoscale pores, resulting in unreliable measurement results. Finally, the optical microscopy method is limited by resolution (usually greater than 0.5μm), making it difficult to accurately detect pores at the micron level and below, and polishing residues can easily cause misjudgment. These limitations make it difficult for existing porosity detection technology to meet the needs of high-precision, non-destructive testing. Therefore, there is an urgent need for a method that can simultaneously obtain porosity values, pore morphology distribution and detailed pore parameters to provide multi-dimensional data support and realize the simultaneous and accurate determination of pore morphology and porosity. Especially in the field of quality inspection of precision materials such as ceramics and aerospace alloys, a more comprehensive and precise technical means is needed to improve the accuracy of material performance prediction, thereby providing more reliable data support for materials science research. Summary of the Invention

[0003] In order to solve the above technical problems and obtain high-precision porosity values, pore morphology distribution and detailed pore parameters, the present invention proposes a pore structure parameter analysis method for metal or ceramic materials, comprising the following steps:

[0004] The material is cut multiple times continuously to obtain multiple material samples; each material sample is processed by a polishing process to obtain a target sample;

[0005] A scanning electron microscope is used to obtain a grayscale image corresponding to each target sample at a preset magnification, thereby obtaining multiple continuous cross-sectional grayscale images. For each cross-sectional grayscale image, a binary image is generated to distinguish between pores and matrix areas by adjusting the image contrast and applying an adaptive threshold segmentation algorithm.

[0006] For each binary image, a connected domain analysis is performed to obtain the structural parameters corresponding to each pore;

[0007] By adjusting the position and orientation of each binary image, all binary images are spatially aligned. Based on the pore position and pore shape, the same pore appearing in adjacent binary images is identified and matched, thereby forming a continuous three-dimensional pore model.

[0008] For the same pore in each binary image, the target structural parameters are obtained using the structural parameters corresponding to the pore in each binary image;

[0009] The corresponding equivalent circle diameter is calculated based on the target structural parameters of each pore; for the region of interest, the porosity corresponding to the region is obtained by taking the ratio of the sum of the pore areas in the region to the area of ​​the region;

[0010] Identifying the pore type corresponding to each pore through a three-dimensional pore model; the pore types include: open pores and closed pores;

[0011] The pore structure parameter data set is formed by porosity, target structural parameters corresponding to each pore, equivalent circle diameter, and pore type.

[0012] Furthermore, each material sample is processed by a polishing process, specifically including: fixing the material sample by hot mounting technology, rough grinding the material sample step by step using silicon carbide sandpaper, then fine polishing with diamond polishing agent, and finally fine polishing the surface using silicon dioxide suspension.

[0013] Furthermore, the structural parameters include: pore area, pore position, pore major axis length and minor axis length;

[0014] The target structural parameters are obtained by using the structural parameters corresponding to the pores in each binary image. Specifically, for the same pore in each binary image, based on its corresponding pore area, major axis length, and minor axis length, the target structural parameters of the pore are determined by the following steps:

[0015] Calculate the average of the pore areas of the pores in each image to obtain the target pore area;

[0016] Calculate the average of the major axis lengths of the pores in each image to obtain the target major axis length;

[0017] The average of the short axis lengths of the pores in each image is calculated to obtain the target short axis length.

[0018] Furthermore, the pore type corresponding to each pore is identified by the three-dimensional pore model, specifically: by using the three-dimensional pore model, it is determined whether at least one end of each pore is connected to the material surface, the pores with at least one end connected to the material surface are determined as open pores, and the pores with neither end connected to the material surface are determined as closed pores.

[0019] Furthermore, the pore structure parameter data set also includes: an equivalent circle diameter distribution histogram;

[0020] The method for obtaining the equivalent circle diameter distribution histogram is:

[0021] Set multiple continuous pore diameter intervals, and obtain the number of pores and the total pore area in each pore diameter interval based on the equivalent circle diameter;

[0022] The histogram of the equivalent circle diameter distribution is drawn with the pore diameter range as the horizontal axis and the pore number or total pore area as the vertical axis.

[0023] Furthermore, the pore structure parameter data set also includes: a pore area distribution histogram;

[0024] The method for obtaining the pore area distribution histogram is:

[0025] Set multiple continuous pore area intervals and count the number of pores falling into each pore area interval;

[0026] The pore area distribution histogram is drawn with the pore area interval as the horizontal axis and the pore number as the vertical axis.

[0027] Furthermore, the hot mounting technique is used to fix the material sample, specifically:

[0028] Hot mounting of material specimens using epoxy resin as a fixing material;

[0029] The hot-mounted material sample was placed in an environment with a curing temperature of 170°C and a pressure of 1200 daN was applied to it;

[0030] Keep warm under the above conditions for 400 seconds.

[0031] Furthermore, the material sample is coarsely ground step by step using silicon carbide sandpaper, specifically:

[0032] First, the material sample was preliminarily ground using 200# silicon carbide sandpaper, and then the material sample was ground step by step using 500# and 1200# silicon carbide sandpaper in sequence to gradually reduce the surface roughness and remove the scratches left by the previous level of sandpaper.

[0033] Furthermore, the fine polishing using a diamond polishing agent is specifically as follows:

[0034] The ground material samples were finely polished using diamond polishing agents with particle sizes of 9 μm, 3 μm, and 1 μm in succession.

[0035] Furthermore, the adaptive threshold segmentation algorithm is specifically the Bernsen local threshold method.

[0036] Compared with the prior art, the present invention has at least the following beneficial effects:

[0037] (1) The present invention includes: performing multiple continuous cutting of a material to obtain multiple samples, each sample being polished in turn to obtain a high-quality target sample; using a scanning electron microscope to obtain a grayscale image of each target sample at a preset magnification to generate multiple continuous cross-sectional grayscale images; generating a binary image by adjusting the image contrast and applying an adaptive threshold segmentation algorithm, and then performing a connected domain analysis to extract the structural parameters of each pore; identifying and matching the same pore appearing on adjacent binary images to form a continuous three-dimensional pore model, and determining the pore type based on the model; finally, generating a pore structure parameter data set by calculating the equivalent circle diameter, the sum of the pore area and the ratio of the area. These steps ensure the simultaneous and accurate determination of pore morphology and porosity, providing more comprehensive and accurate data support for materials science research;

[0038] (2) The present invention can simultaneously obtain the porosity value, pore morphology distribution (equivalent circle diameter distribution histogram and pore area distribution histogram) and detailed pore parameters, realizing the simultaneous and accurate determination of pore morphology and porosity. Compared with traditional methods that can only provide single porosity data, this method provides richer information;

[0039] (3) Through high-resolution imaging using a scanning electron microscope (SEM) and intelligent image analysis technology, this method can not only distinguish between open and closed pores, but also detect micropores (<1 μm) with high sensitivity. This overcomes the shortcomings of existing methods in micropore detection and improves the accuracy and reliability of detection.

[0040] (4) The pore structure parameter dataset provided by the present invention is particularly suitable for the quality inspection of precision materials such as ceramics and aerospace alloys, and provides a more reliable technical means for the microstructure analysis of these materials. This method not only improves the detection accuracy, but also avoids the sample damage and misjudgment problems that may be introduced in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a method for analyzing pore structure parameters of metal or ceramic materials according to an embodiment of the present invention;

[0042] Figure 2 is a cross-sectional grayscale image according to an embodiment of the present invention;

[0043] Figure 3 is a binarized image according to an embodiment of the present invention;

[0044] Figure 4 is a distribution histogram corresponding to the cross-sectional grayscale image in an embodiment of the present invention;

[0045] Figure 5 for Figure 4 Enlargement of the left image;

[0046] Figure 6 for Figure 4 The left image is before it was numbered. DETAILED DESCRIPTION

[0047] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.

[0048] Example 1

[0049] In order to solve the above technical problems and obtain high-precision porosity values, pore morphology distribution and detailed pore parameters, such as Figure 1 As shown, the present invention proposes a method for analyzing pore structure parameters of metal or ceramic materials, comprising the steps of:

[0050] The material is cut multiple times continuously to obtain multiple material samples; each material sample is processed by a polishing process to obtain a target sample;

[0051] Each material sample was treated using a polishing process, specifically including: hot mounting to secure the sample, coarse grinding with silicon carbide sandpaper, fine polishing with a diamond polishing agent, and finally surface polishing with a silica suspension. This process resulted in no visible scratches or other defects on the sample surface when observed under a scanning electron microscope (SEM) at a magnification of 20,000x. This demonstrates the effectiveness of the sample preparation process, which involves coarse grinding with silicon carbide sandpaper, fine polishing with a diamond polishing agent, and final polishing with a silica suspension. This high-quality surface treatment ensures the quality of subsequent high-resolution imaging and provides a reliable foundation for the accurate determination of pore morphology and porosity, making it suitable for in-depth research into the microstructure of metals or ceramics.

[0052] The hot mounting technology is used to fix the material sample, specifically:

[0053] Hot mounting of material specimens using epoxy resin as a fixing material;

[0054] The hot-mounted material sample was placed in an environment with a curing temperature of 170°C and a pressure of 1200 daN was applied to it;

[0055] Keep warm under the above conditions for 400 seconds.

[0056] The silicon carbide sandpaper is used to perform step-by-step coarse grinding on the material sample, specifically:

[0057] First, the material sample was preliminarily ground using 200# silicon carbide sandpaper, and then the material sample was ground step by step using 500# and 1200# silicon carbide sandpaper in sequence to gradually reduce the surface roughness and remove the scratches left by the previous level of sandpaper.

[0058] The diamond polishing agent is used for fine polishing, specifically:

[0059] The ground material samples were finely polished using diamond polishing agents with particle sizes of 9 μm, 3 μm, and 1 μm in succession.

[0060] A scanning electron microscope is used to obtain a grayscale image corresponding to each target sample at a preset magnification, thereby obtaining multiple continuous cross-sectional grayscale images;

[0061] Specifically, a scanning electron microscope (SEM) was used to acquire grayscale images of each target sample at 10,000x and / or 20,000x magnification. During image acquisition, the accelerating voltage was set to 5 kV, and the working distance was adjusted to 10 mm to ensure high-quality cross-sectional grayscale images. This setup not only ensured high-resolution imaging but also provided a reliable basis for accurate determination of pore morphology and porosity.

[0062] It should be noted here that when grayscale images at various magnifications are acquired, images at different magnifications are suitable for different parameter calculations.

[0063] Specifically, grayscale images at higher magnifications (e.g., 20,000x) are more suitable for accurately calculating detailed structural parameters such as pore area, shape, and size due to their high resolution. This is because high-magnification images can more clearly reveal the microscopic details of pores, helping to improve measurement accuracy.

[0064] Grayscale images at lower magnifications (e.g., 10,000x) cover a larger field of view, making them more suitable for calculating macroscopic parameters such as porosity. By using low-magnification images, a holistic view of pore distribution over a larger area can be obtained, allowing for a more accurate assessment of the spatial distribution and overall proportion of pores within the material.

[0065] In summary, multi-magnification SEM imaging effectively combines the advantages of images taken at different magnifications: high-magnification images facilitate the quantification of fine structural parameters, while low-magnification images aid in the determination of macroscopic parameters. This combined approach not only improves the accuracy of parameter measurements but also provides comprehensive data support for a deeper understanding of the material's internal pore structure. Therefore, in practice, the appropriate selection and utilization of images taken at different magnifications is crucial for accurate pore structure analysis.

[0066] It should also be noted that:

[0067] Secondary electron imaging in a scanning electron microscope (SEM) is a commonly used technique that provides high-resolution images of a sample's surface. Secondary electrons are highly sensitive to surface topography and are therefore well-suited for observing microscopic features such as pores within a material's internal structure.

[0068] Based on this, in this embodiment, when using a scanning electron microscope for imaging, selecting the secondary electron detection mode can capture fine details of the sample surface. This is because secondary electrons primarily originate from the interaction region within a few nanometers of the sample surface, making them particularly effective in revealing subtle morphological changes on the sample surface.

[0069] Furthermore, to enhance the distinction between pores and the matrix material, the contrast of the grayscale image needs to be adjusted after acquisition. Contrast adjustment is achieved by increasing or decreasing the grayscale difference between the brightest and darkest parts of the image. This step is crucial for accurate pore identification and analysis.

[0070] Specifically, the goal here is to ensure that the grayscale value difference between the pore area and the matrix material is at least 50%, based on the 256-level grayscale standard (i.e., the grayscale value of each pixel ranges from 0 to 255). This means that if the average grayscale value of the pore area is a certain value, then the average grayscale value of the adjacent matrix material should differ by at least 128 grayscale levels (because 50% of 256 levels is equal to 128). This is done to ensure that there is a clear enough distinction between the pores and the matrix to facilitate subsequent image processing steps, such as the application of adaptive threshold segmentation algorithms, to more accurately extract pore structure parameters.

[0071] This method creates a clear contrast between pores and the matrix material in the image, which is crucial for the subsequent application of an adaptive threshold segmentation algorithm to generate a binary image, perform connected domain analysis, and calculate specific pore structural parameters (such as area, location, major axis length, and minor axis length). This method improves the accuracy of pore identification, helps to construct more accurate 3D pore models, and ultimately obtains reliable porosity data and other relevant parameters.

[0072] For each cross-sectional grayscale image, a binary image for distinguishing pores from matrix areas is generated by adjusting the image contrast and applying an adaptive threshold segmentation algorithm.

[0073] It's important to note that in materials science, the matrix refers to the continuous phase that makes up a composite material or alloy. It's the primary component of the material, within which other phases, such as pores, reinforcement particles, and fibers, are dispersed. For metals or ceramics, the matrix typically refers to an ideal, complete portion of the material, devoid of pores or other inclusions.

[0074] In other words, the matrix refers to the part of the material other than the pores, that is, the solid part that makes up the main body of the material. When using a scanning electron microscope (SEM) to obtain an image of the material cross-section, and for each cross-sectional grayscale image, by adjusting the contrast of the image and applying an adaptive threshold segmentation algorithm, "distinguishing pores from the matrix" means identifying the pore area and the area composed of the material matrix in the image. This step is key to calculating porosity and analyzing pore characteristics because it allows accurate measurement of the area ratio occupied by pores relative to the area of ​​the entire material. In this process, the matrix is ​​regarded as the background or continuous phase, and the pores are the discontinuous phase distributed in the matrix. In this way, the distribution of pores inside the material and its impact on material properties can be accurately evaluated.

[0075] The adaptive threshold segmentation algorithm is specifically the Bernsen local threshold method.

[0076] Specifically, the generation of the binary image includes:

[0077] Preprocess the cross-sectional grayscale image to reduce noise and enhance contrast;

[0078] Apply the adaptive threshold segmentation algorithm on the preprocessed image to calculate the local contrast and local average gray value of each pixel;

[0079] A local threshold for each pixel is determined based on the calculation result, and the image is converted into a binary image accordingly, wherein the pore area is marked with a first value (eg, 0) and the matrix area is marked with a second value (eg, 255).

[0080] For each binary image, a connected domain analysis is performed to obtain the structural parameters corresponding to each pore; the structural parameters include: pore area, pore position, pore major axis length and minor axis length;

[0081] By adjusting the position and orientation of each binary image, all binary images are spatially aligned. Based on the pore position and pore shape, the same pore appearing in adjacent binary images is identified and matched, thereby forming a continuous three-dimensional pore model.

[0082] For the same pore in each binary image, the target structural parameters are obtained using the structural parameters corresponding to the pore in each binary image;

[0083] The target structural parameters are obtained by using the structural parameters corresponding to the pores in each binary image. Specifically, for the same pore in each binary image, based on its corresponding pore area, major axis length, and minor axis length, the target structural parameters of the pore are determined by the following steps:

[0084] Calculate the average of the pore areas of the pores in each image to obtain the target pore area;

[0085] Calculate the average of the major axis lengths of the pores in each image to obtain the target major axis length;

[0086] The average of the short axis lengths of the pores in each image is calculated to obtain the target short axis length.

[0087] The corresponding equivalent circle diameter is calculated based on the target structural parameters of each pore; for the region of interest, the porosity corresponding to the region is obtained by taking the ratio of the sum of the pore areas in the region to the area of ​​the region;

[0088] In this embodiment, the major axis and minor axis of a pore generally refer to the longest diameter (i.e., the distance measured along the longest direction of the pore) and the shortest diameter (i.e., the shortest distance measured perpendicular to the major axis) of the pore in the binary image. These parameters can help more accurately describe the shape of the pore.

[0089] Specifically, the equivalent circle diameter refers to the diameter of a circle when the area of ​​the circle is assumed to be equal to the actual area of ​​the pore. The formula is as follows:

[0090]

[0091] Where A is the pore area, d eq is the equivalent circle diameter.

[0092] If the major axis (a) and minor axis (b) of the pore are known, the equivalent circular diameter of the pore can also be estimated using an elliptical model. In this case, the average diameter of the pore can be calculated first and then converted to an equivalent circular diameter. A common method is to use the geometric mean to approximate the pore size:

[0093]

[0094] For pores that are close to circular, the area-based method may be more accurate; whereas for pores that deviate significantly from a circular shape, the major axis and minor axis-based method may better reflect the actual situation.

[0095] Identifying the pore type corresponding to each pore through a three-dimensional pore model; the pore types include: open pores and closed pores;

[0096] The method of identifying the pore type corresponding to each pore through the three-dimensional pore model is specifically as follows: through the three-dimensional pore model, it is determined whether at least one end of each pore is connected to the material surface, and the pores with at least one end connected to the material surface are determined as open pores, and the pores with neither end connected to the material surface are determined as closed pores.

[0097] This method utilizes high-resolution scanning electron microscopy (SEM) imaging and intelligent image analysis technology to not only distinguish open from closed pores but also detect micropores (<1 μm) with high sensitivity. This overcomes the shortcomings of existing methods in micropore detection and improves the accuracy and reliability of detection.

[0098] The pore structure parameter data set is formed by porosity, target structural parameters corresponding to each pore, equivalent circle diameter, and pore type.

[0099] The pore structure parameter dataset provided by this invention is particularly suitable for the quality inspection of precision materials such as ceramics and aerospace alloys, providing a more reliable technical means for the microstructural analysis of these materials. This method not only improves detection accuracy but also avoids sample damage and misjudgment problems that may be introduced by traditional methods.

[0100] The pore structure parameter data set also includes: an equivalent circle diameter distribution histogram;

[0101] The method for obtaining the equivalent circle diameter distribution histogram is:

[0102] Set multiple continuous pore diameter intervals, and obtain the number of pores and the total pore area in each pore diameter interval based on the equivalent circle diameter;

[0103] The histogram of the equivalent circle diameter distribution is drawn with the pore diameter range as the horizontal axis and the pore number or total pore area as the vertical axis.

[0104] The pore structure parameter data set also includes: a pore area distribution histogram;

[0105] The method for obtaining the pore area distribution histogram is:

[0106] Set multiple continuous pore area intervals and count the number of pores falling into each pore area interval;

[0107] The pore area distribution histogram is drawn with the pore area interval as the horizontal axis and the pore number as the vertical axis.

[0108] The present invention has three major technical advantages:

[0109] First, it can simultaneously obtain porosity values, pore morphology distribution, and detailed pore parameters. Second, compared with the water displacement method, this method can not only distinguish between open and closed pores, but also has a higher resolution for detecting micropores (<1μm). Finally, the pore structure parameter dataset generated by this method provides multidimensional data support for material property prediction. By combining a standardized polishing process, high-resolution scanning electron microscopy (SEM) imaging, and intelligent image analysis technology, this method achieves simultaneous and precise determination of pore morphology and porosity, making it particularly suitable for quality inspection of precision materials such as ceramics and aerospace alloys.

[0110] Example 2

[0111] To further illustrate the accuracy and reliability of the pore structure parameter analysis method of the present invention, this example uses an M-type strontium ferrite ceramic material as the test object, and accurately measures the porosity and its structural parameters through the following specific steps:

[0112] 1. Sample preparation steps:

[0113] A cylindrical sample with a size of 20.89 × 9.09 mm was processed using a MECAPRESS 3 hot-press mounting instrument:

[0114] Epoxy resin was used as the fixing material, and hot mounting was performed at a temperature of 170°C, a curing pressure of 1200 daN, and a holding time of 400 seconds.

[0115] The mounted samples were polished step by step using a Stell Tegramin-25 automatic polishing machine:

[0116] First, use 200# silicon carbide sandpaper for preliminary grinding at a coarse grinding pressure of 20N and a grinding wheel speed of 300rpm. Then use 500# and 1200# silicon carbide sandpaper in sequence to further reduce the surface roughness and remove the scratches left by the previous level of sandpaper.

[0117] Next, fine polishing was performed using diamond polishing compounds with particle sizes of 9 μm, 3 μm, and 1 μm, followed by a final polishing using a 0.25 μm silica suspension (OPS) until a mirror finish was achieved. The pressure during polishing was set at 10 N and the grinding wheel speed was adjusted to 150 rpm.

[0118] 2. Scanning electron microscope imaging steps:

[0119] Images were acquired using a Theisken Clara scanning electron microscope (SEM) in secondary electron (ET) mode. The accelerating voltage was set to 5 kV, the working distance was adjusted to 10 mm, and the scan speed was set to 4. Three different areas on the target specimen were imaged at 10,000x magnification. In addition, high-resolution imaging of specific areas of interest was performed at 20,000x magnification to obtain more detailed pore structure parameters.

[0120] Figure 2 The grayscale image of a cross section of one of the scanned areas is shown. The following is an explanation of the meaning of each English word in the image:

[0121] 1. Mag (Magnification): Magnification. "10.0kx" in the figure indicates that the magnification of the SEM image is 10,000 times.

[0122] 2. FoV (Field of View): Field of view. The figure shows "27.9μm", which means that the actual size of the sample surface observed at the current magnification is 27.9 microns.

[0123] 3. FileName: File name. In the figure, "10KX" is shown. This is the file name used when saving the SEM image.

[0124] 4. WD (Working Distance): Working distance. The figure shows "5.85mm", which means the distance from the electron beam to the sample surface is 5.85 mm.

[0125] 5. Speed: Scanning speed. The figure shows "4", which means that the SEM equipment is set to the 4th speed when acquiring images. This will affect the image resolution and acquisition time.

[0126] 6. BC (Beam Current): Electron beam current. "100pA" in the figure indicates that the electron beam current used for imaging is 100 picoamperes.

[0127] 7. Scan Mode: The image shows "UH-RESOLUTION", which means it uses ultra-high resolution scanning mode to obtain clearer and more detailed images.

[0128] 8. Energy: Acceleration voltage. The figure shows "5keV", which means that the electron beam is accelerated to an energy of 5 kilovolts, which is crucial for penetrating the sample and generating secondary electron signals.

[0129] 9. Time: Imaging time. The image shows "15:49:06", which means the image was acquired at 3:49:06 PM.

[0130] 10. Date: The image shows "2025-03-20", which means the image was collected on March 20, 2025.

[0131] 11. DoF (Depth of Field): Depth of field. The figure shows "5.31μm", indicating that under the current conditions, the sample depth range that can be clearly focused is 5.31 microns.

[0132] The "5 μm" in the image refers to the length of the scale bar in the image. Specifically, it means that the marked distance in the scanning electron microscope (SEM) image corresponds to 5 micrometers (μm) on the actual sample.

[0133] These parameters together determine the quality and detail of the SEM image, ensuring accurate analysis of the pore structure of M-type strontium ferrite ceramics.

[0134] 3. Image analysis steps:

[0135] After the above sample preparation and imaging steps, the contrast was adjusted to ensure that the grayscale value difference between the pores and the matrix was ≥50% (measured on a 256-level grayscale standard). An adaptive threshold segmentation algorithm was then applied to generate a binary image. Connected domain analysis was performed to extract pore structure parameters and calculate the porosity corresponding to each region. The final output porosities were 4.533%, 4.536%, and 4.519%, respectively. These data provide accurate information on the internal pore distribution of the M-type strontium ferrite ceramic sample. At the same time, based on the details provided by the high-magnification images, the specific shape and size parameters of the pores were accurately measured, and a continuous three-dimensional pore model was constructed based on this information to identify pore type (open or closed) and other structural characteristics.

[0136] like Figure 3 As shown, in order to further illustrate the accuracy of the pore structure parameter analysis method, this embodiment also gives Figure 2 The corresponding binary image. The following is an explanation of the meaning of each English word in the figure:

[0137] 1. 27.68 × 27.68 μm: This represents the field of view (FoV) of the image, that is, the actual size of the sample surface observed at the current magnification is 27.68 microns × 27.68 microns.

[0138] 2. (1024x1024): Indicates the resolution of the image, that is, the image consists of 1024 pixels wide and 1024 pixels high.

[0139] 3. 8-bit 1MB: Indicates the image's bit depth and file size. 8-bit means each pixel uses 8 bits to represent the grayscale value, for a total of 256 grayscale levels; 1MB means the image file size is 1 megabyte.

[0140] 4. Results: The results window displays the results of various parameters obtained after image processing.

[0141] 5. Label: Label, used to distinguish different regions. In the figure, "1.tf" represents the first labeled region.

[0142] 6. Area: It indicates the total area of ​​the pores in the marked area. It is shown as "34.717" in the figure. The unit is usually square micrometer (μm 2 ).

[0143] 7. IntDen (Integrated Density): Integrated density is the product of the average grayscale value and the area of ​​the area, reflecting the total light intensity of the area. In the figure, it is displayed as "3185.158".

[0144] 8. %Area: The percentage of total area, indicating the ratio of the total pore area to the entire marked area. In the figure, it is shown as "4.533%".

[0145] 9. RawIntDen (Raw Integrated Density): Raw integrated density, total light intensity without background correction. Displayed as "4360481" in the figure.

[0146] 10. MinThr (Minimum Threshold): The minimum threshold used to convert an image into a binary image. Displayed as "0" in the figure.

[0147] 11. MaxThr (Maximum Threshold): The maximum threshold used to convert the image into a binary image. In the figure, it is shown as "140".

[0148] These parameters collectively describe the characteristics of the pore regions in the binarized image, helping us more accurately analyze pore structure parameters. By adjusting the threshold for binarization, we can clearly distinguish pores from the matrix, allowing us to calculate porosity and other related parameters.

[0149] In order to further illustrate the distribution of pore structure parameters, Figure 4 The histogram analysis results of pore area and pore diameter are shown. The following is a detailed explanation:

[0150] 1. Left image ( Figure 5 is an enlarged view of the left image. Figure 6 (This is the image on the left before it is numbered):

[0151] The image on the left shows the pore distribution after binarization. The yellow areas represent identified pores, and each pore is numbered. This shows that within the 27.68 × 27.68 μm field of view, a large number of pores of varying sizes and shapes were detected.

[0152] 2. Histogram in the upper right corner (pore area distribution):

[0153] Horizontal axis: represents the pore area, the unit is square micrometer (μm 2 ).

[0154] Vertical axis: represents count, that is, the number of pores appearing in the counted area.

[0155] Distribution characteristics: As can be seen from the figure, the area of ​​most pores is concentrated in a smaller range, especially in the range of 0.0 to 0.1 μm. 2 The number of pores is the largest between 0.2 μm and 1600. As the pore area increases, the number of pores decreases rapidly. 2 There are almost no pores above.

[0156] 3. Histogram in the lower right corner (pore diameter distribution):

[0157] Horizontal axis: represents the pore diameter, in micrometers (μm).

[0158] Vertical axis: also represents count, that is, the number of pores appearing in the counted area.

[0159] Distribution characteristics: Similar to the pore area distribution, most pores are concentrated in a smaller diameter range, particularly between 0.0 and 0.1 μm, where the largest number of pores is close to 1200. As the pore diameter increases, the number of pores decreases, and pores are almost non-existent above 0.3 μm.

[0160] 4. Comprehensive analysis:

[0161] From these two histograms, we can draw the following conclusions:

[0162] 4.1. Pore size is mainly concentrated in a small range: Regardless of pore area or pore diameter, the vast majority of pores are located in a smaller size range, indicating that the pores in the material are mainly micro pores.

[0163] 4.2. Uneven pore distribution: Although most pores are small, there are also some larger pores, which may affect certain properties of the material. For example:

[0164] Mechanical properties: Larger pores can act as stress concentration points, reducing the overall strength and toughness of the material. Under stress, these pores can easily trigger crack propagation, leading to premature failure of the material.

[0165] Thermal performance: The presence of large pores affects the thermal conductivity of the material. Since the thermal conductivity of air is much lower than that of solid materials, larger pores increase the insulation effect within the material and reduce the overall thermal conductivity efficiency.

[0166] Electrical properties: For applications sensitive to electrical properties (such as electronic components), pores, especially larger pores, may change the current path, increase resistance or cause local overheating, affecting the stability and reliability of the device.

[0167] 4.3 High data reliability: Since a large amount of pore data is counted, these histograms can more accurately reflect the actual situation of the pore structure in the material.

[0168] In summary, Figure 4 The histograms of pore area and pore diameter show the distribution characteristics of pore structure parameters in detail, providing important data support for subsequent material performance analysis.

[0169] This example not only demonstrates the detailed steps from sample preparation and imaging to image analysis to ensure the accuracy and reliability of porosity testing, but also highlights the advantages of multi-magnification imaging technology in improving the precision of pore structure parameter analysis. This further demonstrates the effectiveness of the method, which is suitable for in-depth research on the microstructure of metal or ceramic materials and quality inspection.

[0170] The present invention includes: cutting a material multiple times continuously to obtain multiple samples, and each sample is polished in turn to obtain a high-quality target sample; using a scanning electron microscope to obtain a grayscale image of each target sample at a preset magnification to generate multiple continuous cross-sectional grayscale images; generating a binary image by adjusting the image contrast and applying an adaptive threshold segmentation algorithm, and then performing a connected domain analysis to extract the structural parameters of each pore; identifying and matching the same pore appearing on adjacent binary images to form a continuous three-dimensional pore model, and determining the pore type accordingly; finally, generating a pore structure parameter data set by calculating the equivalent circle diameter, the sum of the pore area and the ratio of the regional area. These steps ensure the synchronous and accurate measurement of pore morphology and porosity, providing more comprehensive and accurate data support for materials science research.

[0171] In addition, the present invention can simultaneously obtain the porosity value, pore morphology distribution (equivalent circle diameter distribution histogram and pore area distribution histogram) and detailed pore parameters, realizing the simultaneous and accurate measurement of pore morphology and porosity. Compared with traditional methods that can only provide single porosity data, this method provides richer information.

[0172] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0173] In addition, in the present invention, descriptions such as "first," "second," and "one" are for descriptive purposes only and should not be understood to indicate or imply their relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0174] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0175] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A method for analyzing pore structure parameters of metal or ceramic materials, characterized in that: Including steps: The material is cut multiple times continuously to obtain multiple material samples; each material sample is processed by a polishing process to obtain a target sample; A scanning electron microscope is used to obtain a grayscale image corresponding to each target sample at a preset magnification, thereby obtaining multiple continuous cross-sectional grayscale images. For each cross-sectional grayscale image, a binary image is generated to distinguish between pores and matrix areas by adjusting the image contrast and applying an adaptive threshold segmentation algorithm. For each binary image, a connected domain analysis is performed to obtain the structural parameters corresponding to each pore; All binary images are spatially aligned by adjusting the position and orientation of each binary image; Based on the pore position and pore shape, the same pore appearing in adjacent binary images is identified and matched, and a continuous three-dimensional pore model is formed accordingly; For the same pore in each binary image, the target structural parameters are obtained using the structural parameters corresponding to the pore in each binary image; The corresponding equivalent circle diameter is calculated based on the target structural parameters of each pore; for the region of interest, the porosity corresponding to the region is obtained by taking the ratio of the sum of the pore areas in the region to the area of ​​the region; Identifying the pore type corresponding to each pore through a three-dimensional pore model; the pore types include: open pores and closed pores; The pore structure parameter data set is formed by porosity, target structural parameters corresponding to each pore, equivalent circle diameter, and pore type.

2. A pore structure parameter analysis method for metal or ceramic materials according to claim 1, characterized in that: Each material sample is processed by a polishing process, specifically including: fixing the material sample by a hot mounting technique, coarse grinding the material sample step by step using silicon carbide sandpaper, then fine polishing with a diamond polishing agent, and finally fine polishing the surface with a silicon dioxide suspension.

3. The pore structure parameter analysis method for metal or ceramic materials according to claim 1, characterized in that: The structural parameters include: pore area, pore position, pore major axis length and minor axis length; The target structural parameters are obtained by using the structural parameters corresponding to the pores in each binary image. Specifically, for the same pore in each binary image, based on its corresponding pore area, major axis length, and minor axis length, the target structural parameters of the pore are determined by the following steps: Calculate the average of the pore areas of the pores in each image to obtain the target pore area; Calculate the average of the major axis lengths of the pores in each image to obtain the target major axis length; The average of the short axis lengths of the pores in each image is calculated to obtain the target short axis length.

4. A pore structure parameter analysis method for metal or ceramic materials according to claim 3, characterized in that: The method of identifying the pore type corresponding to each pore through the three-dimensional pore model is specifically as follows: through the three-dimensional pore model, it is determined whether at least one end of each pore is connected to the material surface, and the pores with at least one end connected to the material surface are determined as open pores, and the pores with neither end connected to the material surface are determined as closed pores.

5. The pore structure parameter analysis method for metal or ceramic materials according to claim 3, characterized in that: The pore structure parameter data set also includes: an equivalent circle diameter distribution histogram; The method for obtaining the equivalent circle diameter distribution histogram is: Set multiple continuous pore diameter intervals, and obtain the number of pores and the total pore area in each pore diameter interval based on the equivalent circle diameter; The histogram of the equivalent circle diameter distribution is drawn with the pore diameter range as the horizontal axis and the pore number or total pore area as the vertical axis.

6. The method for analyzing pore structure parameters of metal or ceramic materials according to claim 3, characterized in that: The pore structure parameter data set also includes: a pore area distribution histogram; The method for obtaining the pore area distribution histogram is: Set multiple continuous pore area intervals and count the number of pores falling into each pore area interval; The pore area distribution histogram is drawn with the pore area interval as the horizontal axis and the pore number as the vertical axis.

7. The method for analyzing pore structure parameters of metal or ceramic materials according to claim 2, characterized in that: The hot mounting technology is used to fix the material sample, specifically: Hot mounting of material specimens using epoxy resin as a fixing material; The hot-mounted material sample was placed in an environment with a curing temperature of 170°C and a pressure of 1200 daN was applied to it; Keep warm under the above conditions for 400 seconds.

8. The method for analyzing pore structure parameters of metal or ceramic materials according to claim 2, characterized in that: The silicon carbide sandpaper is used to perform step-by-step coarse grinding on the material sample, specifically: First, use 200# silicon carbide sandpaper to perform preliminary grinding on the material sample, and then use 500# and 1200# silicon carbide sandpaper to grind the material sample step by step.

9. The method for analyzing pore structure parameters of metal or ceramic materials according to claim 2, characterized in that: The diamond polishing agent is used for fine polishing, specifically: The ground material samples were finely polished using diamond polishing agents with particle sizes of 9 μm, 3 μm, and 1 μm in succession.

10. The pore structure parameter analysis method for metal or ceramic materials according to claim 1, characterized in that: The adaptive threshold segmentation algorithm is specifically the Bernsen local threshold method.

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